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Table 1 Demographic and clinical information for the sample of 894 participants of healthy control and stroke

From: The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke

 

Control (n = 465)

Stroke (n = 429)

Age

51 (20–88)

63 (18–92)

Sex

244 M, 221 F

280 M, 149 F

Dominant Hand

434 R, 29 L, 2 A

393 R, 33 L, 3 A

Days since Stroke

\(\cdots\)

17 (1–34)

Types of Stroke

\(\cdots\)

370 I, 59 H

TLT [0, 1, 2, 3]

 Affected Side

\(\cdots\)

[210, 104, 73, 30]*

CMSA [1–7]

 Affected Arm

\(\cdots\)

[29, 33, 51, 58, 61, 80, 103]

 Unaffected Arm

\(\cdots\)

[0, 0, 0, 0, 15, 94, 320]

CMSA [1–7]

 Affected Hand

\(\cdots\)

[31, 33, 37, 43, 74, 93, 104]

 Unaffected Hand

\(\cdots\)

[0, 0, 0, 0, 7, 125, 297]

PPB

 Affected Side

\(\cdots\)

6.9 (0–17.5)

 Unaffected Side

\(\cdots\)

10.5 (2.5–19)

 FIM (Total Score)

\(\cdots\)

93.7 (37–126)

 FIM (Motor)

\(\cdots\)

65 (13–91)

  1. Data are presented as the mean (range) unless otherwise noted. Square brackets for TLT, CMSA scores indicate the actual number of individuals who obtained a given score on the test, e.g., 210 individuals scored 0 on the TLT
  2. M—Male; F—Female; R—Right; L—Light; A—Ambidextrous; H—Hemorrhagic; I—Ischemic; TLT—Thumb Localizing Test; CSMA—Chedoke-McMaster Stroke Assessment; PPB—Purdue Peg Board; and FIM—Functional Independence Measure
  3. *2 scores were missing
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Journal of NeuroEngineering and Rehabilitation

ISSN: 1743-0003

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